Subsurface Transport of Sulfidated Nano Zero Valent Iron and In Situ Biogeochemical Transformation of Chlorinated Solvents: A Field Study
Notice bibliographique
Résumé
In situ chemical reduction of chlorinated volatile organic compounds (cVOCs) by nano zero-valent iron (nZVI) has been widely applied in the past 20 years, but with limited effectiveness for bare nZVI due to rapid particle settling, short lifespan, and low reactivity. Stabilization and sulfidation of nZVI have improved its mobility and longevity, increased reactivity towards cVOCs, and reduced toxicity to microbes (Nunez Garcia et al. 2021). In the first-ever CMC-S-nZVI field trial, nZVI sulfidated by dithionite (S-nZVI) and stabilized by carboxymethyl cellulose (CMC) was injected into the subsurface of a site contaminated with a wide range of cVOCs. Multi-level wells were installed to monitor the transport of the CMC-S-nZVI suspension and its remedial performance for two years. Short-term (0-17 days) monitoring demonstrated a good transport of the suspension in the down- and up-gradient wells, in terms of total iron, boron, and sulfides which were major constituents of CMC-S-nZVI. Changes in concentrations of parent compounds, intermediates, and ethene showed effective dechlorination of high-chlorinated VOCs such as tetrachloroethene (PCE) and carbon tetrachloride (Nunez Garcia et al. 2020a, Nunez Garcia et al. 2020b). Long-term (157-729 days) performance was evaluated through temporal analyses of microbial communities, total iron, boron, and cVOCs in groundwater samples. Microbial populations, including organohalide-respiring bacteria, increased by >1 order of magnitude; with Geobacter being the most abundant. This long-term enrichment can be attributed to the low toxicity of CMC-S-nZVI and biostimulation by CMC and perhaps Fe 3+ . Non-metric multidimensional scaling analysis was carried out on microbial data grouped by depth range and proximity to the injection well. At locations that clearly received CMC-S-nZVI, there was a significant shift in microbial communities that was sustained for the long term. Iron concentrations increased substantially in long-term samples while boron concentrations decreased, suggesting that this iron did not come from CMC-S-nZVI. Microbial dissolution of iron minerals might have contributed to the increased iron content (Jones et al. 2006). Excess dithionite in CMC-S-nZVI would also have reductively dissolved native iron from the soil, as successfully demonstrated in the in situ redox manipulation (ISRM) technology wherein subsurface Fe 3+ in soil was reduced to Fe 2+ for long-term remedial purposes (Vermeul et al. 2000). Long-term changes in concentrations of lesser-chlorinated VOCs and hydrocarbons suggest that PCE was degraded via both the microbially-mediated sequential hydrogenolysis as well as the abiotic β-elimination. The intermediate vinyl chloride (VC) surprisingly did not accumulate in the current study, in contrast to the significant VC accumulation in a previous un-sulfidated CMC-nZVI trial at the same location (Kocur et al. 2016). Excess dithionite injected in this study might have avoided VC accumulation, as previously reported for ISRM treatment of a cVOCs-contaminated site (Vermeul et al. 2000). Additionally, the identified bacterial populations might have utilized sulfur species (from dithionite decomposition) and iron to form iron sulfides, which could dechlorinate cVOCs via in situ biogeochemical transformation (Kennedy et al. 2006). In summary, this study has demonstrated the long-term efficiency of CMC-S-nZVI for cVOCs removal through a combination of abiotic, biotic, and biostimulatory processes in the subsurface.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».